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Some fixes.

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Ubuntu 2023-06-13 14:08:37 +00:00 committed by Nicolas Patry
parent a0a194c391
commit ae308f88ec
4 changed files with 348 additions and 626 deletions
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531
server/text_generation_server/utils/gptq/quant_linear.py
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@ -12,121 +12,66 @@ try:
# code based https://github.com/fpgaminer/GPTQ-triton # code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune( @custom_autotune.autotune(
configs=[ configs=[
triton.Config( triton.Config({
{ 'BLOCK_SIZE_M': 64,
"BLOCK_SIZE_M": 64, 'BLOCK_SIZE_N': 256,
"BLOCK_SIZE_N": 256, 'BLOCK_SIZE_K': 32,
"BLOCK_SIZE_K": 32, 'GROUP_SIZE_M': 8
"GROUP_SIZE_M": 8, }, num_stages=4, num_warps=4),
}, triton.Config({
num_stages=4, 'BLOCK_SIZE_M': 128,
num_warps=4, 'BLOCK_SIZE_N': 128,
), 'BLOCK_SIZE_K': 32,
triton.Config( 'GROUP_SIZE_M': 8
{ }, num_stages=4, num_warps=4),
"BLOCK_SIZE_M": 128, triton.Config({
"BLOCK_SIZE_N": 128, 'BLOCK_SIZE_M': 64,
"BLOCK_SIZE_K": 32, 'BLOCK_SIZE_N': 128,
"GROUP_SIZE_M": 8, 'BLOCK_SIZE_K': 32,
}, 'GROUP_SIZE_M': 8
num_stages=4, }, num_stages=4, num_warps=4),
num_warps=4, triton.Config({
), 'BLOCK_SIZE_M': 128,
triton.Config( 'BLOCK_SIZE_N': 32,
{ 'BLOCK_SIZE_K': 32,
"BLOCK_SIZE_M": 64, 'GROUP_SIZE_M': 8
"BLOCK_SIZE_N": 128, }, num_stages=4, num_warps=4),
"BLOCK_SIZE_K": 32, triton.Config({
"GROUP_SIZE_M": 8, 'BLOCK_SIZE_M': 64,
}, 'BLOCK_SIZE_N': 64,
num_stages=4, 'BLOCK_SIZE_K': 32,
num_warps=4, 'GROUP_SIZE_M': 8
), }, num_stages=4, num_warps=4),
triton.Config( triton.Config({
{ 'BLOCK_SIZE_M': 64,
"BLOCK_SIZE_M": 128, 'BLOCK_SIZE_N': 128,
"BLOCK_SIZE_N": 32, 'BLOCK_SIZE_K': 32,
"BLOCK_SIZE_K": 32, 'GROUP_SIZE_M': 8
"GROUP_SIZE_M": 8, }, num_stages=2, num_warps=8),
}, triton.Config({
num_stages=4, 'BLOCK_SIZE_M': 64,
num_warps=4, 'BLOCK_SIZE_N': 64,
), 'BLOCK_SIZE_K': 64,
triton.Config( 'GROUP_SIZE_M': 8
{ }, num_stages=3, num_warps=8),
"BLOCK_SIZE_M": 64, triton.Config({
"BLOCK_SIZE_N": 64, 'BLOCK_SIZE_M': 32,
"BLOCK_SIZE_K": 32, 'BLOCK_SIZE_N': 32,
"GROUP_SIZE_M": 8, 'BLOCK_SIZE_K': 128,
}, 'GROUP_SIZE_M': 8
num_stages=4, }, num_stages=2, num_warps=4),
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=2,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=3,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=2,
num_warps=4,
),
], ],
key=["M", "N", "K"], key=['M', 'N', 'K'],
nearest_power_of_two=True, nearest_power_of_two=True,
prune_configs_by={ prune_configs_by={
"early_config_prune": custom_autotune.matmul248_kernel_config_pruner, 'early_config_prune': custom_autotune.matmul248_kernel_config_pruner,
"perf_model": None, 'perf_model': None,
"top_k": None, 'top_k': None,
}, },
) )
@triton.jit @triton.jit
def matmul_248_kernel( def matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros,
a_ptr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
b_ptr,
c_ptr,
scales_ptr,
zeros_ptr,
g_ptr,
M,
N,
K,
bits,
maxq,
stride_am,
stride_ak,
stride_bk,
stride_bn,
stride_cm,
stride_cn,
stride_scales,
stride_zeros,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
):
""" """
Compute the matrix multiplication C = A x B. Compute the matrix multiplication C = A x B.
A is of shape (M, K) float16 A is of shape (M, K) float16
@ -134,7 +79,7 @@ try:
C is of shape (M, N) float16 C is of shape (M, N) float16
scales is of shape (G, N) float16 scales is of shape (G, N) float16
zeros is of shape (G, N) float16 zeros is of shape (G, N) float16
g_ptr is of shape (K) int32 g_ptr is of shape (K) int32
""" """
infearure_per_bits = 32 // bits infearure_per_bits = 32 // bits
@ -152,15 +97,10 @@ try:
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K) offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + ( a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak a_mask = (offs_am[:, None] < M)
) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
a_mask = offs_am[:, None] < M
# b_ptrs is set up such that it repeats elements along the K axis 8 times # b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + ( b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
(offs_k[:, None] // infearure_per_bits) * stride_bk
+ offs_bn[None, :] * stride_bn
) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_k g_ptrs = g_ptr + offs_k
# shifter is used to extract the N bits of each element in the 32-bit word from B # shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_bn[None, :] scales_ptrs = scales_ptr + offs_bn[None, :]
@ -174,17 +114,13 @@ try:
g_idx = tl.load(g_ptrs) g_idx = tl.load(g_ptrs)
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
scales = tl.load( scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
scales_ptrs + g_idx[:, None] * stride_scales zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(
zeros_ptrs + g_idx[:, None] * stride_zeros
) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = zeros + 1 zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values # Now we need to unpack b (which is N-bit values) into 32-bit values
@ -200,118 +136,61 @@ try:
c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
tl.store(c_ptrs, accumulator, mask=c_mask) tl.store(c_ptrs, accumulator, mask=c_mask)
@custom_autotune.autotune( @custom_autotune.autotune(configs=[
configs=[ triton.Config({
triton.Config( 'BLOCK_SIZE_M': 64,
{ 'BLOCK_SIZE_N': 32,
"BLOCK_SIZE_M": 64, 'BLOCK_SIZE_K': 256,
"BLOCK_SIZE_N": 32, 'GROUP_SIZE_M': 8
"BLOCK_SIZE_K": 256, }, num_stages=4, num_warps=4),
"GROUP_SIZE_M": 8, triton.Config({
}, 'BLOCK_SIZE_M': 128,
num_stages=4, 'BLOCK_SIZE_N': 32,
num_warps=4, 'BLOCK_SIZE_K': 128,
), 'GROUP_SIZE_M': 8
triton.Config( }, num_stages=4, num_warps=4),
{ triton.Config({
"BLOCK_SIZE_M": 128, 'BLOCK_SIZE_M': 64,
"BLOCK_SIZE_N": 32, 'BLOCK_SIZE_N': 32,
"BLOCK_SIZE_K": 128, 'BLOCK_SIZE_K': 128,
"GROUP_SIZE_M": 8, 'GROUP_SIZE_M': 8
}, }, num_stages=4, num_warps=4),
num_stages=4, triton.Config({
num_warps=4, 'BLOCK_SIZE_M': 128,
), 'BLOCK_SIZE_N': 32,
triton.Config( 'BLOCK_SIZE_K': 32,
{ 'GROUP_SIZE_M': 8
"BLOCK_SIZE_M": 64, }, num_stages=4, num_warps=4),
"BLOCK_SIZE_N": 32, triton.Config({
"BLOCK_SIZE_K": 128, 'BLOCK_SIZE_M': 64,
"GROUP_SIZE_M": 8, 'BLOCK_SIZE_N': 32,
}, 'BLOCK_SIZE_K': 64,
num_stages=4, 'GROUP_SIZE_M': 8
num_warps=4, }, num_stages=4, num_warps=4),
), triton.Config({
triton.Config( 'BLOCK_SIZE_M': 64,
{ 'BLOCK_SIZE_N': 32,
"BLOCK_SIZE_M": 128, 'BLOCK_SIZE_K': 128,
"BLOCK_SIZE_N": 32, 'GROUP_SIZE_M': 8
"BLOCK_SIZE_K": 32, }, num_stages=2, num_warps=8),
"GROUP_SIZE_M": 8, triton.Config({
}, 'BLOCK_SIZE_M': 64,
num_stages=4, 'BLOCK_SIZE_N': 64,
num_warps=4, 'BLOCK_SIZE_K': 64,
), 'GROUP_SIZE_M': 8
triton.Config( }, num_stages=3, num_warps=8),
{ triton.Config({
"BLOCK_SIZE_M": 64, 'BLOCK_SIZE_M': 32,
"BLOCK_SIZE_N": 32, 'BLOCK_SIZE_N': 128,
"BLOCK_SIZE_K": 64, 'BLOCK_SIZE_K': 32,
"GROUP_SIZE_M": 8, 'GROUP_SIZE_M': 8
}, }, num_stages=2, num_warps=4),
num_stages=4, ],
num_warps=4, key=['M', 'N', 'K'],
), nearest_power_of_two=True)
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=2,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=3,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=2,
num_warps=4,
),
],
key=["M", "N", "K"],
nearest_power_of_two=True,
)
@triton.jit @triton.jit
def transpose_matmul_248_kernel( def transpose_matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales,
a_ptr, stride_zeros, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
b_ptr,
c_ptr,
scales_ptr,
zeros_ptr,
g_ptr,
M,
N,
K,
bits,
maxq,
stride_am,
stride_ak,
stride_bk,
stride_bn,
stride_cm,
stride_cn,
stride_scales,
stride_zeros,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
):
""" """
Compute the matrix multiplication C = A x B. Compute the matrix multiplication C = A x B.
A is of shape (M, N) float16 A is of shape (M, N) float16
@ -319,7 +198,7 @@ try:
C is of shape (M, K) float16 C is of shape (M, K) float16
scales is of shape (G, N) float16 scales is of shape (G, N) float16
zeros is of shape (G, N) float16 zeros is of shape (G, N) float16
g_ptr is of shape (K) int32 g_ptr is of shape (K) int32
""" """
infearure_per_bits = 32 // bits infearure_per_bits = 32 // bits
@ -337,25 +216,16 @@ try:
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
offs_n = tl.arange(0, BLOCK_SIZE_N) offs_n = tl.arange(0, BLOCK_SIZE_N)
a_ptrs = a_ptr + ( a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak a_mask = (offs_am[:, None] < M)
) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
a_mask = offs_am[:, None] < M
# b_ptrs is set up such that it repeats elements along the K axis 8 times # b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + ( b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
(offs_bk[:, None] // infearure_per_bits) * stride_bk
+ offs_n[None, :] * stride_bn
) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_bk g_ptrs = g_ptr + offs_bk
g_idx = tl.load(g_ptrs) g_idx = tl.load(g_ptrs)
# shifter is used to extract the N bits of each element in the 32-bit word from B # shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
zeros_ptrs = ( zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
zeros_ptr
+ (offs_n[None, :] // infearure_per_bits)
+ g_idx[:, None] * stride_zeros
)
shifter = (offs_bk % infearure_per_bits) * bits shifter = (offs_bk % infearure_per_bits) * bits
zeros_shifter = (offs_n % infearure_per_bits) * bits zeros_shifter = (offs_n % infearure_per_bits) * bits
@ -367,9 +237,9 @@ try:
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = zeros + 1 zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_N) a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values # Now we need to unpack b (which is N-bit values) into 32-bit values
@ -381,84 +251,36 @@ try:
a_ptrs += BLOCK_SIZE_N a_ptrs += BLOCK_SIZE_N
b_ptrs += BLOCK_SIZE_N b_ptrs += BLOCK_SIZE_N
scales_ptrs += BLOCK_SIZE_N scales_ptrs += BLOCK_SIZE_N
zeros_ptrs += BLOCK_SIZE_N // infearure_per_bits zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :] c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K) c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
tl.store(c_ptrs, accumulator, mask=c_mask) tl.store(c_ptrs, accumulator, mask=c_mask)
except: except:
print("triton not installed.") print('triton not installed.')
def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
with torch.cuda.device(input.device): with torch.cuda.device(input.device):
output = torch.empty( output = torch.empty((input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16)
(input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16 grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']), )
) matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, input.stride(0), input.stride(1), qweight.stride(0),
grid = lambda META: ( qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0))
triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
* triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
)
matmul_248_kernel[grid](
input,
qweight,
output,
scales,
qzeros,
g_idx,
input.shape[0],
qweight.shape[1],
input.shape[1],
bits,
maxq,
input.stride(0),
input.stride(1),
qweight.stride(0),
qweight.stride(1),
output.stride(0),
output.stride(1),
scales.stride(0),
qzeros.stride(0),
)
return output return output
def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
with torch.cuda.device(input.device): with torch.cuda.device(input.device):
output_dim = (qweight.shape[0] * 32) // bits output_dim = (qweight.shape[0] * 32) // bits
output = torch.empty( output = torch.empty((input.shape[0], output_dim), device=input.device, dtype=torch.float16)
(input.shape[0], output_dim), device=input.device, dtype=torch.float16 grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']), )
) transpose_matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], output_dim, bits, maxq, input.stride(0), input.stride(1), qweight.stride(0),
grid = lambda META: ( qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0))
triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
* triton.cdiv(output_dim, META["BLOCK_SIZE_K"]),
)
transpose_matmul_248_kernel[grid](
input,
qweight,
output,
scales,
qzeros,
g_idx,
input.shape[0],
qweight.shape[1],
output_dim,
bits,
maxq,
input.stride(0),
input.stride(1),
qweight.stride(0),
qweight.stride(1),
output.stride(0),
output.stride(1),
scales.stride(0),
qzeros.stride(0),
)
return output return output
class QuantLinearFunction(torch.autograd.Function): class QuantLinearFunction(torch.autograd.Function):
@staticmethod @staticmethod
@custom_fwd(cast_inputs=torch.float16) @custom_fwd(cast_inputs=torch.float16)
def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq): def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
@ -475,9 +297,7 @@ class QuantLinearFunction(torch.autograd.Function):
grad_input = None grad_input = None
if ctx.needs_input_grad[0]: if ctx.needs_input_grad[0]:
grad_input = transpose_matmul248( grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
grad_output, qweight, scales, qzeros, g_idx, bits, maxq
)
return grad_input, None, None, None, None, None, None return grad_input, None, None, None, None, None, None
@ -500,39 +320,72 @@ class QuantLinear(nn.Module):
@classmethod @classmethod
def new(cls, bits, groupsize, infeatures, outfeatures, bias): def new(cls, bits, groupsize, infeatures, outfeatures, bias):
super().__init__()
if bits not in [2, 4, 8]: if bits not in [2, 4, 8]:
raise NotImplementedError("Only 2,4,8 bits are supported.") raise NotImplementedError("Only 2,4,8 bits are supported.")
qweight = torch.zeros(
(infeatures // 32 * self.bits, outfeatures), dtype=torch.int32 qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
) qzeros = torch.zeros((math.ceil(infeatures / groupsize), outfeatures // 32 * bits), dtype=torch.int32)
qzeros = torch.zeros( scales = torch.zeros((math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16)
(math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), g_idx = torch.tensor([i // groupsize for i in range(infeatures)], dtype=torch.int32)
dtype=torch.int32,
)
scales = torch.zeros(
(math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16
)
g_idx = torch.tensor(
[i // self.groupsize for i in range(infeatures)], dtype=torch.int32
)
if bias: if bias:
bias = torch.zeros((outfeatures), dtype=torch.float16) bias = torch.zeros((outfeatures), dtype=torch.float16)
else: else:
bias = None bias = None
return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize) return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
def pack(self, linear, scales, zeros, g_idx=None):
self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
scales = scales.t().contiguous()
zeros = zeros.t().contiguous()
scale_zeros = zeros * scales
self.scales = scales.clone().half()
if linear.bias is not None:
self.bias = linear.bias.clone().half()
intweight = []
for idx in range(self.infeatures):
intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(torch.int)[:, None])
intweight = torch.cat(intweight, dim=1)
intweight = intweight.t().contiguous()
intweight = intweight.numpy().astype(np.uint32)
qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
i = 0
row = 0
while row < qweight.shape[0]:
if self.bits in [2, 4, 8]:
for j in range(i, i + (32 // self.bits)):
qweight[row] |= intweight[j] << (self.bits * (j - i))
i += 32 // self.bits
row += 1
else:
raise NotImplementedError("Only 2,4,8 bits are supported.")
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
zeros -= 1
zeros = zeros.numpy().astype(np.uint32)
qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
i = 0
col = 0
while col < qzeros.shape[1]:
if self.bits in [2, 4, 8]:
for j in range(i, i + (32 // self.bits)):
qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
i += 32 // self.bits
col += 1
else:
raise NotImplementedError("Only 2,4,8 bits are supported.")
qzeros = qzeros.astype(np.int32)
self.qzeros = torch.from_numpy(qzeros)
def forward(self, x): def forward(self, x):
out_shape = x.shape[:-1] + (self.outfeatures,) out_shape = x.shape[:-1] + (self.outfeatures, )
out = QuantLinearFunction.apply( out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, self.g_idx, self.bits, self.maxq)
x.reshape(-1, x.shape[-1]),
self.qweight,
self.scales,
self.qzeros,
self.g_idx,
self.bits,
self.maxq,
)
out = out + self.bias if self.bias is not None else out out = out + self.bias if self.bias is not None else out
return out.reshape(out_shape) return out.reshape(out_shape)

409
server/text_generation_server/utils/gptq/quantize.py
View File

@ -4,36 +4,30 @@ import numpy as np
import torch import torch
import torch.nn as nn import torch.nn as nn
import math import math
import json
import os import os
from texttable import Texttable from texttable import Texttable
from transformers import AutoModelForCausalLM from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
import transformers import transformers
import numpy as np import numpy as np
import torch import torch
from text_generation_server.utils.gptq.quant_linear import QuantLinear from text_generation_server.utils.gptq.quant_linear import QuantLinear
from loguru import logger
DEV = torch.device("cuda:0") DEV = torch.device("cuda:0")
class Quantizer(nn.Module): class Quantizer(nn.Module):
def __init__(self, shape=1): def __init__(self, shape=1):
super(Quantizer, self).__init__() super(Quantizer, self).__init__()
self.register_buffer("maxq", torch.tensor(0)) self.register_buffer('maxq', torch.tensor(0))
self.register_buffer("scale", torch.zeros(shape)) self.register_buffer('scale', torch.zeros(shape))
self.register_buffer("zero", torch.zeros(shape)) self.register_buffer('zero', torch.zeros(shape))
def configure(self, bits, perchannel=False, sym=True, mse=False, norm=2.4, grid=100, maxshrink=.8, trits=False):
def configure(
self,
bits,
perchannel=False,
sym=True,
mse=False,
norm=2.4,
grid=100,
maxshrink=0.8,
trits=False,
):
self.maxq = torch.tensor(2**bits - 1) self.maxq = torch.tensor(2**bits - 1)
self.perchannel = perchannel self.perchannel = perchannel
self.sym = sym self.sym = sym
@ -94,16 +88,14 @@ class Quantizer(nn.Module):
self.zero = torch.round(-xmin / self.scale) self.zero = torch.round(-xmin / self.scale)
if self.mse: if self.mse:
best = torch.full([x.shape[0]], float("inf"), device=dev) best = torch.full([x.shape[0]], float('inf'), device=dev)
for i in range(int(self.maxshrink * self.grid)): for i in range(int(self.maxshrink * self.grid)):
p = 1 - i / self.grid p = 1 - i / self.grid
xmin1 = p * xmin xmin1 = p * xmin
xmax1 = p * xmax xmax1 = p * xmax
scale1 = (xmax1 - xmin1) / self.maxq scale1 = (xmax1 - xmin1) / self.maxq
zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
q = self._quantize( q = self._quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq
)
q -= x q -= x
q.abs_() q.abs_()
q.pow_(self.norm) q.pow_(self.norm)
@ -150,6 +142,7 @@ class Quantizer(nn.Module):
class GPTQ: class GPTQ:
def __init__(self, layer, observe=False): def __init__(self, layer, observe=False):
self.layer = layer self.layer = layer
self.dev = self.layer.weight.device self.dev = self.layer.weight.device
@ -177,19 +170,12 @@ class GPTQ:
if len(inp.shape) == 2: if len(inp.shape) == 2:
inp = inp.unsqueeze(0) inp = inp.unsqueeze(0)
tmp = inp.shape[0] tmp = inp.shape[0]
if isinstance(self.layer, nn.Linear) or isinstance( if isinstance(self.layer, nn.Linear) or isinstance(self.layer, transformers.Conv1D):
self.layer, transformers.Conv1D
):
if len(inp.shape) == 3: if len(inp.shape) == 3:
inp = inp.reshape((-1, inp.shape[-1])) inp = inp.reshape((-1, inp.shape[-1]))
inp = inp.t() inp = inp.t()
if isinstance(self.layer, nn.Conv2d): if isinstance(self.layer, nn.Conv2d):
unfold = nn.Unfold( unfold = nn.Unfold(self.layer.kernel_size, dilation=self.layer.dilation, padding=self.layer.padding, stride=self.layer.stride)
self.layer.kernel_size,
dilation=self.layer.dilation,
padding=self.layer.padding,
stride=self.layer.stride,
)
inp = unfold(inp) inp = unfold(inp)
inp = inp.permute([1, 0, 2]) inp = inp.permute([1, 0, 2])
inp = inp.flatten(1) inp = inp.flatten(1)
@ -202,14 +188,12 @@ class GPTQ:
def print_loss(self, name, q_weight, weight_error, timecost): def print_loss(self, name, q_weight, weight_error, timecost):
table = Texttable() table = Texttable()
name += " " * (16 - len(name)) name += ' ' * (16 - len(name))
table.header(["name", "weight_error", "fp_inp_SNR", "q_inp_SNR", "time"]) table.header(['name', 'weight_error', 'fp_inp_SNR', 'q_inp_SNR', 'time'])
# assign weight # assign weight
self.layer.weight.data = q_weight.reshape(self.layer.weight.shape).to( self.layer.weight.data = q_weight.reshape(self.layer.weight.shape).to(self.layer.weight.data.dtype)
self.layer.weight.data.dtype
)
if self.inp1 is not None: if self.inp1 is not None:
# quantize input to int8 # quantize input to int8
@ -223,15 +207,13 @@ class GPTQ:
q_SNR = torch_snr_error(q_out, self.out1).item() q_SNR = torch_snr_error(q_out, self.out1).item()
fp_SNR = torch_snr_error(self.layer(self.inp1), self.out1).item() fp_SNR = torch_snr_error(self.layer(self.inp1), self.out1).item()
else: else:
q_SNR = "-" q_SNR = '-'
fp_SNR = "-" fp_SNR = '-'
table.add_row([name, weight_error, fp_SNR, q_SNR, timecost]) table.add_row([name, weight_error, fp_SNR, q_SNR, timecost])
print(table.draw().split("\n")[-2]) print(table.draw().split('\n')[-2])
def fasterquant( def fasterquant(self, blocksize=128, percdamp=.01, groupsize=-1, actorder=False, name=''):
self, blocksize=128, percdamp=0.01, groupsize=-1, actorder=False, name=""
):
self.layer.to(self.dev) self.layer.to(self.dev)
W = self.layer.weight.data.clone() W = self.layer.weight.data.clone()
@ -290,9 +272,7 @@ class GPTQ:
if groupsize != -1: if groupsize != -1:
if (i1 + i) % groupsize == 0: if (i1 + i) % groupsize == 0:
self.quantizer.find_params( self.quantizer.find_params(W[:, (i1 + i):(i1 + i + groupsize)], weight=True)
W[:, (i1 + i) : (i1 + i + groupsize)], weight=True
)
if ((i1 + i) // groupsize) - now_idx == -1: if ((i1 + i) // groupsize) - now_idx == -1:
scale.append(self.quantizer.scale) scale.append(self.quantizer.scale)
@ -301,7 +281,7 @@ class GPTQ:
q = self.quantizer.quantize(w.unsqueeze(1)).flatten() q = self.quantizer.quantize(w.unsqueeze(1)).flatten()
Q1[:, i] = q Q1[:, i] = q
Losses1[:, i] = (w - q) ** 2 / d**2 Losses1[:, i] = (w - q)**2 / d**2
err1 = (w - q) / d err1 = (w - q) / d
W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0)) W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
@ -326,9 +306,7 @@ class GPTQ:
if isinstance(self.layer, transformers.Conv1D): if isinstance(self.layer, transformers.Conv1D):
Q = Q.t() Q = Q.t()
self.print_loss( self.print_loss(name=name, q_weight=Q, weight_error=error, timecost=(time.time() - tick))
name=name, q_weight=Q, weight_error=error, timecost=(time.time() - tick)
)
if scale == []: if scale == []:
scale.append(self.quantizer.scale) scale.append(self.quantizer.scale)
@ -348,18 +326,15 @@ class GPTQ:
def get_wikitext2(nsamples, seed, seqlen, model_id): def get_wikitext2(nsamples, seed, seqlen, model_id):
from datasets import load_dataset from datasets import load_dataset
traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')
traindata = load_dataset("wikitext", "wikitext-2-raw-v1", split="train") testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
from transformers import AutoTokenizer from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
trainenc = tokenizer("\n\n".join(traindata["text"]), return_tensors="pt") trainenc = tokenizer("\n\n".join(traindata['text']), return_tensors='pt')
testenc = tokenizer("\n\n".join(testdata["text"]), return_tensors="pt") testenc = tokenizer("\n\n".join(testdata['text']), return_tensors='pt')
import random import random
random.seed(seed) random.seed(seed)
trainloader = [] trainloader = []
for _ in range(nsamples): for _ in range(nsamples):
@ -374,21 +349,18 @@ def get_wikitext2(nsamples, seed, seqlen, model_id):
def get_ptb(nsamples, seed, seqlen, model_id): def get_ptb(nsamples, seed, seqlen, model_id):
from datasets import load_dataset from datasets import load_dataset
traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train')
traindata = load_dataset("ptb_text_only", "penn_treebank", split="train") valdata = load_dataset('ptb_text_only', 'penn_treebank', split='validation')
valdata = load_dataset("ptb_text_only", "penn_treebank", split="validation")
from transformers import AutoTokenizer from transformers import AutoTokenizer
try: try:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
except: except:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
trainenc = tokenizer("\n\n".join(traindata["sentence"]), return_tensors="pt") trainenc = tokenizer("\n\n".join(traindata['sentence']), return_tensors='pt')
testenc = tokenizer("\n\n".join(valdata["sentence"]), return_tensors="pt") testenc = tokenizer("\n\n".join(valdata['sentence']), return_tensors='pt')
import random import random
random.seed(seed) random.seed(seed)
trainloader = [] trainloader = []
for _ in range(nsamples): for _ in range(nsamples):
@ -403,37 +375,22 @@ def get_ptb(nsamples, seed, seqlen, model_id):
def get_c4(nsamples, seed, seqlen, model_id): def get_c4(nsamples, seed, seqlen, model_id):
from datasets import load_dataset from datasets import load_dataset
traindata = load_dataset('allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', use_auth_token=False)
traindata = load_dataset( valdata = load_dataset('allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation', use_auth_token=False)
"allenai/c4",
"allenai--c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
use_auth_token=False,
)
valdata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
split="validation",
use_auth_token=False,
)
from transformers import AutoTokenizer from transformers import AutoTokenizer
try: try:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
except: except:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
import random import random
random.seed(seed) random.seed(seed)
trainloader = [] trainloader = []
for _ in range(nsamples): for _ in range(nsamples):
while True: while True:
i = random.randint(0, len(traindata) - 1) i = random.randint(0, len(traindata) - 1)
trainenc = tokenizer(traindata[i]["text"], return_tensors="pt") trainenc = tokenizer(traindata[i]['text'], return_tensors='pt')
if trainenc.input_ids.shape[1] >= seqlen: if trainenc.input_ids.shape[1] >= seqlen:
break break
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1) i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
@ -444,13 +401,12 @@ def get_c4(nsamples, seed, seqlen, model_id):
trainloader.append((inp, tar)) trainloader.append((inp, tar))
import random import random
random.seed(0) random.seed(0)
valenc = [] valenc = []
for _ in range(256): for _ in range(256):
while True: while True:
i = random.randint(0, len(valdata) - 1) i = random.randint(0, len(valdata) - 1)
tmp = tokenizer(valdata[i]["text"], return_tensors="pt") tmp = tokenizer(valdata[i]['text'], return_tensors='pt')
if tmp.input_ids.shape[1] >= seqlen: if tmp.input_ids.shape[1] >= seqlen:
break break
i = random.randint(0, tmp.input_ids.shape[1] - seqlen - 1) i = random.randint(0, tmp.input_ids.shape[1] - seqlen - 1)
@ -459,6 +415,7 @@ def get_c4(nsamples, seed, seqlen, model_id):
valenc = torch.hstack(valenc) valenc = torch.hstack(valenc)
class TokenizerWrapper: class TokenizerWrapper:
def __init__(self, input_ids): def __init__(self, input_ids):
self.input_ids = input_ids self.input_ids = input_ids
@ -469,21 +426,18 @@ def get_c4(nsamples, seed, seqlen, model_id):
def get_ptb_new(nsamples, seed, seqlen, model_id): def get_ptb_new(nsamples, seed, seqlen, model_id):
from datasets import load_dataset from datasets import load_dataset
traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train')
traindata = load_dataset("ptb_text_only", "penn_treebank", split="train") testdata = load_dataset('ptb_text_only', 'penn_treebank', split='test')
testdata = load_dataset("ptb_text_only", "penn_treebank", split="test")
from transformers import AutoTokenizer from transformers import AutoTokenizer
try: try:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
except: except:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
trainenc = tokenizer(" ".join(traindata["sentence"]), return_tensors="pt") trainenc = tokenizer(" ".join(traindata['sentence']), return_tensors='pt')
testenc = tokenizer(" ".join(testdata["sentence"]), return_tensors="pt") testenc = tokenizer(" ".join(testdata['sentence']), return_tensors='pt')
import random import random
random.seed(seed) random.seed(seed)
trainloader = [] trainloader = []
for _ in range(nsamples): for _ in range(nsamples):
@ -498,35 +452,22 @@ def get_ptb_new(nsamples, seed, seqlen, model_id):
def get_c4_new(nsamples, seed, seqlen, model_id): def get_c4_new(nsamples, seed, seqlen, model_id):
from datasets import load_dataset from datasets import load_dataset
traindata = load_dataset('allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')
traindata = load_dataset( valdata = load_dataset('allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')
"allenai/c4",
"allenai--c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
)
valdata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
split="validation",
)
from transformers import AutoTokenizer from transformers import AutoTokenizer
try: try:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
except: except:
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
import random import random
random.seed(seed) random.seed(seed)
trainloader = [] trainloader = []
for _ in range(nsamples): for _ in range(nsamples):
while True: while True:
i = random.randint(0, len(traindata) - 1) i = random.randint(0, len(traindata) - 1)
trainenc = tokenizer(traindata[i]["text"], return_tensors="pt") trainenc = tokenizer(traindata[i]['text'], return_tensors='pt')
if trainenc.input_ids.shape[1] >= seqlen: if trainenc.input_ids.shape[1] >= seqlen:
break break
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1) i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
@ -536,10 +477,11 @@ def get_c4_new(nsamples, seed, seqlen, model_id):
tar[:, :-1] = -100 tar[:, :-1] = -100
trainloader.append((inp, tar)) trainloader.append((inp, tar))
valenc = tokenizer(" ".join(valdata[:1100]["text"]), return_tensors="pt") valenc = tokenizer(' '.join(valdata[:1100]['text']), return_tensors='pt')
valenc = valenc.input_ids[:, : (256 * seqlen)] valenc = valenc.input_ids[:, :(256 * seqlen)]
class TokenizerWrapper: class TokenizerWrapper:
def __init__(self, input_ids): def __init__(self, input_ids):
self.input_ids = input_ids self.input_ids = input_ids
@ -548,46 +490,31 @@ def get_c4_new(nsamples, seed, seqlen, model_id):
return trainloader, valenc return trainloader, valenc
def get_loaders(name, nsamples=128, seed=0, seqlen=2048, model_id=""): def get_loaders(name, nsamples=128, seed=0, seqlen=2048, model_id=''):
if "wikitext2" in name: if 'wikitext2' in name:
return get_wikitext2(nsamples, seed, seqlen, model_id) return get_wikitext2(nsamples, seed, seqlen, model_id)
if "ptb" in name: if 'ptb' in name:
if "new" in name: if 'new' in name:
return get_ptb_new(nsamples, seed, seqlen, model_id) return get_ptb_new(nsamples, seed, seqlen, model_id)
return get_ptb(nsamples, seed, seqlen, model_id) return get_ptb(nsamples, seed, seqlen, model_id)
if "c4" in name: if 'c4' in name:
if "new" in name: if 'new' in name:
return get_c4_new(nsamples, seed, seqlen, model_id) return get_c4_new(nsamples, seed, seqlen, model_id)
return get_c4(nsamples, seed, seqlen, model_id) return get_c4(nsamples, seed, seqlen, model_id)
def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=""): def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
# Skip last lm_head linear # Skip last lm_head linear
if type(module) in layers and "lm_head" not in name: if type(module) in layers and "lm_head" not in name:
return {name: module} return {name: module}
res = {} res = {}
for name1, child in module.named_children(): for name1, child in module.named_children():
res.update( res.update(find_layers(child, layers=layers, name=name + '.' + name1 if name != '' else name1))
find_layers(
child, layers=layers, name=name + "." + name1 if name != "" else name1
)
)
return res return res
@torch.no_grad() @torch.no_grad()
def sequential( def sequential(model, dataloader, dev, nsamples, bits, groupsize, percdamp=0.01, sym: bool=False, act_order: bool = False):
model, print('Starting ...')
dataloader,
dev,
nsamples,
bits,
groupsize,
percdamp=0.01,
sym: bool = False,
act_order: bool = False,
):
print("Starting ...")
use_cache = model.config.use_cache use_cache = model.config.use_cache
model.config.use_cache = False model.config.use_cache = False
@ -601,21 +528,20 @@ def sequential(
# layers[0] = layers[0].to(dev) # layers[0] = layers[0].to(dev)
dtype = next(iter(model.parameters())).dtype dtype = next(iter(model.parameters())).dtype
inps = torch.zeros( inps = torch.zeros((nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev)
(nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev cache = {'i': 0, 'attention_mask': None}
)
cache = {"i": 0, "attention_mask": None}
class Catcher(nn.Module): class Catcher(nn.Module):
def __init__(self, module): def __init__(self, module):
super().__init__() super().__init__()
self.module = module self.module = module
def forward(self, inp, **kwargs): def forward(self, inp, **kwargs):
inps[cache["i"]] = inp inps[cache['i']] = inp
cache["i"] += 1 cache['i'] += 1
cache["attention_mask"] = kwargs["attention_mask"] cache['attention_mask'] = kwargs['attention_mask']
cache["position_ids"] = kwargs["position_ids"] cache['position_ids'] = kwargs['position_ids']
raise ValueError raise ValueError
layers[0] = Catcher(layers[0]) layers[0] = Catcher(layers[0])
@ -632,20 +558,19 @@ def sequential(
torch.cuda.empty_cache() torch.cuda.empty_cache()
outs = torch.zeros_like(inps) outs = torch.zeros_like(inps)
attention_mask = cache["attention_mask"].to(dev) attention_mask = cache['attention_mask'].to(dev)
position_ids = cache["position_ids"].to(dev) position_ids = cache['position_ids'].to(dev)
print("Ready.") print('Ready.')
quantizers = {} quantizers = {}
for i in range(len(layers)): for i in range(len(layers)):
print(f"Quantizing layer {i+1}/{len(layers)}..") print(f'Quantizing layer {i+1}/{len(layers)}..')
print("+------------------+--------------+------------+-----------+-------+") print('+------------------+--------------+------------+-----------+-------+')
print("| name | weight_error | fp_inp_SNR | q_inp_SNR | time |") print('| name | weight_error | fp_inp_SNR | q_inp_SNR | time |')
print("+==================+==============+============+===========+=======+") print('+==================+==============+============+===========+=======+')
from accelerate.hooks import remove_hook_from_submodules from accelerate.hooks import remove_hook_from_submodules
layer = layers[i].to(dev) layer = layers[i].to(dev)
remove_hook_from_submodules(layer) remove_hook_from_submodules(layer)
full = find_layers(layer) full = find_layers(layer)
@ -656,11 +581,10 @@ def sequential(
gptq = {} gptq = {}
for name in subset: for name in subset:
gptq[name] = GPTQ(subset[name]) gptq[name] = GPTQ(subset[name])
gptq[name].quantizer.configure( gptq[name].quantizer.configure(bits, perchannel=True, sym=sym, mse=False)
bits, perchannel=True, sym=sym, mse=False
)
def add_batch(name): def add_batch(name):
def tmp(_, inp, out): def tmp(_, inp, out):
gptq[name].add_batch(inp[0].data, out.data) gptq[name].add_batch(inp[0].data, out.data)
@ -670,38 +594,19 @@ def sequential(
for name in subset: for name in subset:
handles.append(subset[name].register_forward_hook(add_batch(name))) handles.append(subset[name].register_forward_hook(add_batch(name)))
for j in range(nsamples): for j in range(nsamples):
outs[j] = layer(
inps[j].unsqueeze(0), outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids=position_ids)[0]
attention_mask=attention_mask,
position_ids=position_ids,
)[0]
for h in handles: for h in handles:
h.remove() h.remove()
for name in subset: for name in subset:
scale, zero, g_idx, error = gptq[name].fasterquant( scale, zero, g_idx, error = gptq[name].fasterquant(percdamp=percdamp, groupsize=groupsize, actorder=act_order, name=name)
percdamp=percdamp, quantizers['model.layers.%d.%s' % (i, name)] = (gptq[name].quantizer.cpu(), scale.cpu(), zero.cpu(), g_idx.cpu(), bits, groupsize)
groupsize=groupsize,
actorder=act_order,
name=name,
)
quantizers["model.layers.%d.%s" % (i, name)] = (
gptq[name].quantizer.cpu(),
scale.cpu(),
zero.cpu(),
g_idx.cpu(),
bits,
groupsize,
)
gptq[name].free() gptq[name].free()
for j in range(nsamples): for j in range(nsamples):
outs[j] = layer( outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids=position_ids)[0]
inps[j].unsqueeze(0),
attention_mask=attention_mask,
position_ids=position_ids,
)[0]
layers[i] = layer.cpu() layers[i] = layer.cpu()
del layer del layer
@ -709,8 +614,8 @@ def sequential(
torch.cuda.empty_cache() torch.cuda.empty_cache()
inps, outs = outs, inps inps, outs = outs, inps
print("+------------------+--------------+------------+-----------+-------+") print('+------------------+--------------+------------+-----------+-------+')
print("\n") print('\n')
# if args.observe: # if args.observe:
# observer.print() # observer.print()
@ -754,34 +659,34 @@ def sequential(
# @torch.no_grad() # @torch.no_grad()
# def llama_eval(model, testenc, dev): # def llama_eval(model, testenc, dev):
# print('Evaluating ...') # print('Evaluating ...')
# #
# testenc = testenc.input_ids # testenc = testenc.input_ids
# nsamples = testenc.numel() // model.seqlen # nsamples = testenc.numel() // model.seqlen
# #
# use_cache = model.config.use_cache # use_cache = model.config.use_cache
# model.config.use_cache = False # model.config.use_cache = False
# layers = model.model.layers # layers = model.model.layers
# #
# model.model.embed_tokens = model.model.embed_tokens.to(dev) # model.model.embed_tokens = model.model.embed_tokens.to(dev)
# layers[0] = layers[0].to(dev) # layers[0] = layers[0].to(dev)
# #
# dtype = next(iter(model.parameters())).dtype # dtype = next(iter(model.parameters())).dtype
# inps = torch.zeros((nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev) # inps = torch.zeros((nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev)
# cache = {'i': 0, 'attention_mask': None} # cache = {'i': 0, 'attention_mask': None}
# #
# class Catcher(nn.Module): # class Catcher(nn.Module):
# #
# def __init__(self, module): # def __init__(self, module):
# super().__init__() # super().__init__()
# self.module = module # self.module = module
# #
# def forward(self, inp, **kwargs): # def forward(self, inp, **kwargs):
# inps[cache['i']] = inp # inps[cache['i']] = inp
# cache['i'] += 1 # cache['i'] += 1
# cache['attention_mask'] = kwargs['attention_mask'] # cache['attention_mask'] = kwargs['attention_mask']
# cache['position_ids'] = kwargs['position_ids'] # cache['position_ids'] = kwargs['position_ids']
# raise ValueError # raise ValueError
# #
# layers[0] = Catcher(layers[0]) # layers[0] = Catcher(layers[0])
# for i in range(nsamples): # for i in range(nsamples):
# batch = testenc[:, (i * model.seqlen):((i + 1) * model.seqlen)].to(dev) # batch = testenc[:, (i * model.seqlen):((i + 1) * model.seqlen)].to(dev)
@ -790,19 +695,19 @@ def sequential(
# except ValueError: # except ValueError:
# pass # pass
# layers[0] = layers[0].module # layers[0] = layers[0].module
# #
# layers[0] = layers[0].cpu() # layers[0] = layers[0].cpu()
# model.model.embed_tokens = model.model.embed_tokens.cpu() # model.model.embed_tokens = model.model.embed_tokens.cpu()
# torch.cuda.empty_cache() # torch.cuda.empty_cache()
# #
# outs = torch.zeros_like(inps) # outs = torch.zeros_like(inps)
# attention_mask = cache['attention_mask'] # attention_mask = cache['attention_mask']
# position_ids = cache['position_ids'] # position_ids = cache['position_ids']
# #
# for i in range(len(layers)): # for i in range(len(layers)):
# print(i) # print(i)
# layer = layers[i].to(dev) # layer = layers[i].to(dev)
# #
# if args.nearest: # if args.nearest:
# subset = find_layers(layer) # subset = find_layers(layer)
# for name in subset: # for name in subset:
@ -811,18 +716,18 @@ def sequential(
# W = subset[name].weight.data # W = subset[name].weight.data
# quantizer.find_params(W, weight=True) # quantizer.find_params(W, weight=True)
# subset[name].weight.data = quantizer.quantize(W).to(next(iter(layer.parameters())).dtype) # subset[name].weight.data = quantizer.quantize(W).to(next(iter(layer.parameters())).dtype)
# #
# for j in range(nsamples): # for j in range(nsamples):
# outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids=position_ids)[0] # outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids=position_ids)[0]
# layers[i] = layer.cpu() # layers[i] = layer.cpu()
# del layer # del layer
# torch.cuda.empty_cache() # torch.cuda.empty_cache()
# inps, outs = outs, inps # inps, outs = outs, inps
# #
# if model.model.norm is not None: # if model.model.norm is not None:
# model.model.norm = model.model.norm.to(dev) # model.model.norm = model.model.norm.to(dev)
# model.lm_head = model.lm_head.to(dev) # model.lm_head = model.lm_head.to(dev)
# #
# testenc = testenc.to(dev) # testenc = testenc.to(dev)
# nlls = [] # nlls = []
# for i in range(nsamples): # for i in range(nsamples):
@ -838,33 +743,21 @@ def sequential(
# nlls.append(neg_log_likelihood) # nlls.append(neg_log_likelihood)
# ppl = torch.exp(torch.stack(nlls).sum() / (nsamples * model.seqlen)) # ppl = torch.exp(torch.stack(nlls).sum() / (nsamples * model.seqlen))
# print(ppl.item()) # print(ppl.item())
# #
# model.config.use_cache = use_cache # model.config.use_cache = use_cache
def make_quant_linear(module, names, bits, groupsize, name=''):
def make_quant_linear(module, names, bits, groupsize, name=""):
if isinstance(module, QuantLinear): if isinstance(module, QuantLinear):
return return
for attr in dir(module): for attr in dir(module):
tmp = getattr(module, attr) tmp = getattr(module, attr)
name1 = name + "." + attr if name != "" else attr name1 = name + '.' + attr if name != '' else attr
if name1 in names: if name1 in names:
delattr(module, attr) delattr(module, attr)
setattr( setattr(module, attr, QuantLinear.new(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
module,
attr,
QuantLinear.new(
bits,
groupsize,
tmp.in_features,
tmp.out_features,
tmp.bias is not None,
),
)
for name1, child in module.named_children(): for name1, child in module.named_children():
make_quant_linear( make_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
child, names, bits, groupsize, name + "." + name1 if name != "" else name1
)
# TODO: perform packing on GPU # TODO: perform packing on GPU
@ -873,26 +766,26 @@ def pack(model, quantizers, bits, groupsize):
layers = {n: layers[n] for n in quantizers} layers = {n: layers[n] for n in quantizers}
make_quant_linear(model, quantizers, bits, groupsize) make_quant_linear(model, quantizers, bits, groupsize)
qlayers = find_layers(model, [QuantLinear]) qlayers = find_layers(model, [QuantLinear])
print("Packing ...") print('Packing ...')
for name in qlayers: for name in qlayers:
print(name) print(name)
quantizers[name], scale, zero, g_idx, _, _ = quantizers[name] quantizers[name], scale, zero, g_idx, _, _ = quantizers[name]
qlayers[name].pack(layers[name], scale, zero, g_idx) qlayers[name].pack(layers[name], scale, zero, g_idx)
print("Done.") print('Done.')
return model return model
# def load_quant(model, checkpoint, bits, groupsize=-1, fused_mlp=True, eval=True, warmup_autotune=True): # def load_quant(model, checkpoint, bits, groupsize=-1, fused_mlp=True, eval=True, warmup_autotune=True):
# from transformers import LlamaConfig, LlamaForCausalLM, modeling_utils # from transformers import LlamaConfig, LlamaForCausalLM, modeling_utils
# config = LlamaConfig.from_pretrained(model) # config = LlamaConfig.from_pretrained(model)
# #
# def noop(*args, **kwargs): # def noop(*args, **kwargs):
# pass # pass
# #
# torch.nn.init.kaiming_uniform_ = noop # torch.nn.init.kaiming_uniform_ = noop
# torch.nn.init.uniform_ = noop # torch.nn.init.uniform_ = noop
# torch.nn.init.normal_ = noop # torch.nn.init.normal_ = noop
# #
# torch.set_default_dtype(torch.half) # torch.set_default_dtype(torch.half)
# modeling_utils._init_weights = False # modeling_utils._init_weights = False
# torch.set_default_dtype(torch.half) # torch.set_default_dtype(torch.half)
@ -905,29 +798,29 @@ def pack(model, quantizers, bits, groupsize):
# if name in layers: # if name in layers:
# del layers[name] # del layers[name]
# quant.make_quant_linear(model, layers, bits, groupsize) # quant.make_quant_linear(model, layers, bits, groupsize)
# #
# del layers # del layers
# #
# print('Loading model ...') # print('Loading model ...')
# if checkpoint.endswith('.safetensors'): # if checkpoint.endswith('.safetensors'):
# from safetensors.torch import load_file as safe_load # from safetensors.torch import load_file as safe_load
# model.load_state_dict(safe_load(checkpoint)) # model.load_state_dict(safe_load(checkpoint))
# else: # else:
# model.load_state_dict(torch.load(checkpoint)) # model.load_state_dict(torch.load(checkpoint))
# #
# if eval: # if eval:
# quant.make_quant_attn(model) # quant.make_quant_attn(model)
# quant.make_quant_norm(model) # quant.make_quant_norm(model)
# if fused_mlp: # if fused_mlp:
# quant.make_fused_mlp(model) # quant.make_fused_mlp(model)
# #
# if warmup_autotune: # if warmup_autotune:
# quant.autotune_warmup_linear(model, transpose=not (eval)) # quant.autotune_warmup_linear(model, transpose=not (eval))
# if eval and fused_mlp: # if eval and fused_mlp:
# quant.autotune_warmup_fused(model) # quant.autotune_warmup_fused(model)
# model.seqlen = 2048 # model.seqlen = 2048
# print('Done.') # print('Done.')
# #
# return model # return model
@ -937,33 +830,33 @@ def pack(model, quantizers, bits, groupsize):
# model.model.norm = model.model.norm.to(gpus[0]) # model.model.norm = model.model.norm.to(gpus[0])
# import copy # import copy
# model.lm_head = copy.deepcopy(model.lm_head).to(gpus[0]) # model.lm_head = copy.deepcopy(model.lm_head).to(gpus[0])
# #
# cache = {'mask': None, 'position_ids': None} # cache = {'mask': None, 'position_ids': None}
# #
# class MoveModule(nn.Module): # class MoveModule(nn.Module):
# #
# def __init__(self, module, invalidate_cache): # def __init__(self, module, invalidate_cache):
# super().__init__() # super().__init__()
# self.module = module # self.module = module
# self.dev = next(iter(self.module.parameters())).device # self.dev = next(iter(self.module.parameters())).device
# self.invalidate_cache=invalidate_cache # self.invalidate_cache=invalidate_cache
# #
# def forward(self, *inp, **kwargs): # def forward(self, *inp, **kwargs):
# inp = list(inp) # inp = list(inp)
# if inp[0].device != self.dev: # if inp[0].device != self.dev:
# inp[0] = inp[0].to(self.dev) # inp[0] = inp[0].to(self.dev)
# #
# if cache['mask'] is None or cache['mask'].device != self.dev or self.invalidate_cache: # if cache['mask'] is None or cache['mask'].device != self.dev or self.invalidate_cache:
# cache['mask'] = kwargs['attention_mask'].to(self.dev) # cache['mask'] = kwargs['attention_mask'].to(self.dev)
# kwargs['attention_mask'] = cache['mask'] # kwargs['attention_mask'] = cache['mask']
# #
# if cache['position_ids'] is None or cache['position_ids'].device != self.dev or self.invalidate_cache: # if cache['position_ids'] is None or cache['position_ids'].device != self.dev or self.invalidate_cache:
# cache['position_ids'] = kwargs['position_ids'].to(self.dev) # cache['position_ids'] = kwargs['position_ids'].to(self.dev)
# kwargs['position_ids'] = cache['position_ids'] # kwargs['position_ids'] = cache['position_ids']
# #
# tmp = self.module(*inp, **kwargs) # tmp = self.module(*inp, **kwargs)
# return tmp # return tmp
# #
# layers = model.model.layers # layers = model.model.layers
# from math import ceil # from math import ceil
# if not gpu_dist: # if not gpu_dist:
@ -975,49 +868,49 @@ def pack(model, quantizers, bits, groupsize):
# assigned_gpus = [0] * (gpu_dist[0]-1) # assigned_gpus = [0] * (gpu_dist[0]-1)
# for i in range(1, len(gpu_dist)): # for i in range(1, len(gpu_dist)):
# assigned_gpus = assigned_gpus + [i] * gpu_dist[i] # assigned_gpus = assigned_gpus + [i] * gpu_dist[i]
# #
# remaining_assignments = len(layers)-len(assigned_gpus) - 1 # remaining_assignments = len(layers)-len(assigned_gpus) - 1
# if remaining_assignments > 0: # if remaining_assignments > 0:
# assigned_gpus = assigned_gpus + [-1] * remaining_assignments # assigned_gpus = assigned_gpus + [-1] * remaining_assignments
# #
# assigned_gpus = assigned_gpus + [0] # assigned_gpus = assigned_gpus + [0]
# #
# for i in range(len(layers)): # for i in range(len(layers)):
# layers[i] = MoveModule(layers[i].to(gpus[assigned_gpus[i]]), i==0) # layers[i] = MoveModule(layers[i].to(gpus[assigned_gpus[i]]), i==0)
# #
# model.gpus = gpus # model.gpus = gpus
# #
# #
# def benchmark(model, input_ids, check=False): # def benchmark(model, input_ids, check=False):
# input_ids = input_ids.to(model.gpus[0] if hasattr(model, 'gpus') else DEV) # input_ids = input_ids.to(model.gpus[0] if hasattr(model, 'gpus') else DEV)
# torch.cuda.synchronize() # torch.cuda.synchronize()
# #
# cache = {'past': None} # cache = {'past': None}
# #
# def clear_past(i): # def clear_past(i):
# #
# def tmp(layer, inp, out): # def tmp(layer, inp, out):
# if cache['past']: # if cache['past']:
# cache['past'][i] = None # cache['past'][i] = None
# #
# return tmp # return tmp
# #
# for i, layer in enumerate(model.model.layers): # for i, layer in enumerate(model.model.layers):
# layer.register_forward_hook(clear_past(i)) # layer.register_forward_hook(clear_past(i))
# #
# print('Benchmarking ...') # print('Benchmarking ...')
# #
# if check: # if check:
# loss = nn.CrossEntropyLoss() # loss = nn.CrossEntropyLoss()
# tot = 0. # tot = 0.
# #
# def sync(): # def sync():
# if hasattr(model, 'gpus'): # if hasattr(model, 'gpus'):
# for gpu in model.gpus: # for gpu in model.gpus:
# torch.cuda.synchronize(gpu) # torch.cuda.synchronize(gpu)
# else: # else:
# torch.cuda.synchronize() # torch.cuda.synchronize()
# #
# max_memory = 0 # max_memory = 0
# with torch.no_grad(): # with torch.no_grad():
# attention_mask = torch.ones((1, input_ids.numel()), device=DEV) # attention_mask = torch.ones((1, input_ids.numel()), device=DEV)
@ -1046,9 +939,7 @@ def pack(model, quantizers, bits, groupsize):
def quantize(model_id: str, bits: int, groupsize: int, output_dir: str): def quantize(model_id: str, bits: int, groupsize: int, output_dir: str):
print("loading model") print("loading model")
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="balanced_low_0")
model_id, torch_dtype=torch.float16, device_map="balanced_low_0"
)
print("LOADED model") print("LOADED model")
model.seqlen = 2048 model.seqlen = 2048
@ -1056,9 +947,8 @@ def quantize(model_id: str, bits: int, groupsize: int, output_dir: str):
nsamples = 128 nsamples = 128
seed = None seed = None
dataloader, testloader = get_loaders(
dataset, nsamples=nsamples, seed=seed, model_id=model_id, seqlen=model.seqlen dataloader, testloader = get_loaders(dataset, nsamples=nsamples, seed=seed, model_id=model_id, seqlen=model.seqlen)
)
tick = time.time() tick = time.time()
quantizers = sequential(model, dataloader, DEV, nsamples, bits, groupsize) quantizers = sequential(model, dataloader, DEV, nsamples, bits, groupsize)
@ -1082,7 +972,7 @@ def quantize(model_id: str, bits: int, groupsize: int, output_dir: str):
# dataloader, testloader = get_loaders(dataset, seed=args.seed, model=args.model, seqlen=model.seqlen) # dataloader, testloader = get_loaders(dataset, seed=args.seed, model=args.model, seqlen=model.seqlen)
# print(dataset) # print(dataset)
# llama_eval(model, testloader, DEV) # llama_eval(model, testloader, DEV)
# #
# if args.test_generation: # if args.test_generation:
# gpus = [torch.device('cuda:%d' % i) for i in range(torch.cuda.device_count())] # gpus = [torch.device('cuda:%d' % i) for i in range(torch.cuda.device_count())]
# if len(gpus) > 1: # if len(gpus) > 1:
@ -1096,7 +986,8 @@ def quantize(model_id: str, bits: int, groupsize: int, output_dir: str):
# streamer = TextStreamer(tokenizer) # streamer = TextStreamer(tokenizer)
# with torch.no_grad(): # with torch.no_grad():
# generated_ids = model.generate(input_ids, streamer=streamer) # generated_ids = model.generate(input_ids, streamer=streamer)
# #
# if args.quant_directory is not None: # if args.quant_directory is not None:
# export_quant_table(quantizers, args.quant_directory) # export_quant_table(quantizers, args.quant_directory)
@ -1109,32 +1000,22 @@ def quantize(model_id: str, bits: int, groupsize: int, output_dir: str):
pack(model, quantizers, bits, groupsize) pack(model, quantizers, bits, groupsize)
from safetensors.torch import save_file from safetensors.torch import save_file
from transformers.modeling_utils import shard_checkpoint from transformers.modeling_utils import shard_checkpoint
state_dict = model.state_dict() state_dict = model.state_dict()
state_dict = {k: v.cpu().contiguous() for k, v in state_dict.items()} state_dict = {k: v.cpu().contiguous() for k, v in state_dict.items()}
state_dict["gptq_bits"] = torch.LongTensor(bits) state_dict["gptq_bits"] = torch.LongTensor(bits)
state_dict["gptq_groupsize"] = torch.LongTensor(groupsize) state_dict["gptq_groupsize"] = torch.LongTensor(groupsize)
shards, index = shard_checkpoint( max_shard_size = "10GB"
state_dict, max_shard_size="10GB", weights_name="model.safetensors" shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name="model.safetensors")
)
os.makedirs(output_dir, exist_ok=True) os.makedirs(output_dir, exist_ok=True)
for shard_file, shard in shards.items(): for shard_file, shard in shards.items():
save_file( save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt", "quantized": "gptq", "origin": "text-generation-inference"})
shard,
os.path.join(output_dir, shard_file),
metadata={
"format": "pt",
"quantized": "gptq",
"origin": "text-generation-inference",
},
)
if index is None: if index is None:
path_to_weights = os.path.join(save_directory, "model.safetensors") path_to_weights = os.path.join(output_dir, "model.safetensors")
logger.info(f"Model weights saved in {path_to_weights}") logger.info(f"Model weights saved in {path_to_weights}")
else: else:
save_index_file = "model.safetensors.index.json" save_index_file = "model.safetensors.index.json"
save_index_file = os.path.join(save_directory, save_index_file) save_index_file = os.path.join(output_dir, save_index_file)
with open(save_index_file, "w", encoding="utf-8") as f: with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n" content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content) f.write(content)

10
server/text_generation_server/utils/layers.py
View File

@ -134,15 +134,13 @@ def get_linear(weight, bias, quantize):
try: try:
qweight, qzeros, scales, g_idx, bits, groupsize = weight qweight, qzeros, scales, g_idx, bits, groupsize = weight
except Exception: except Exception:
raise NotImplementedError( raise NotImplementedError(f"The passed weight is not `gptq` compatible, loader needs to be updated.")
f"The passed weight is not `gptq` compatible, loader needs to be updated."
)
linear = QuantLinear( linear = QuantLinear(
qweight, qweight,
qzeros, qzeros,
scales, scales,
g_idx, g_idx,
bias, bias,
bits, bits,
groupsize, groupsize,
@ -223,7 +221,7 @@ class TensorParallelColumnLinear(SuperLayer):
@classmethod @classmethod
def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int): def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
weight = weights.get_multi_weight_col(prefixes, quantize=config.quantize) weight = weights.get_multi_weights_col(prefixes, quantize=config.quantize)
if bias: if bias:
b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes] b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
@ -241,7 +239,7 @@ class TensorParallelRowLinear(SuperLayer):
@classmethod @classmethod
def load(cls, config, prefix: str, weights, bias: bool): def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weight_row(prefix, quantize=config.quantize) weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0: if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process # Rank is only on the first rank process

24
server/text_generation_server/utils/weights.py
View File

@ -86,20 +86,12 @@ class Weights:
def get_multi_weights_col(self, prefixes: List[str], quantize: str): def get_multi_weights_col(self, prefixes: List[str], quantize: str):
if quantize == "gptq": if quantize == "gptq":
try: try:
qweight = torch.cat( qweight = torch.cat([self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1)
[self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1
)
except RuntimeError: except RuntimeError:
raise RuntimeError( raise RuntimeError("Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`")
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
)
qzeros = torch.cat( qzeros = torch.cat([self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1)
[self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1 scales = torch.cat([self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1)
)
scales = torch.cat(
[self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
)
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes] w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]: for w2 in w[1:]:
torch.testing.assert_close(w2, w[0]) torch.testing.assert_close(w2, w[0])
@ -110,17 +102,15 @@ class Weights:
weight = (qweight, qzeros, scales, g_idx, bits, groupsize) weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
else: else:
w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes] w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
weight = torch.cat(w, dim=dim) weight = torch.cat(w, dim=1)
return weight return weight
def get_multi_self_row(self, prefix: str, quantize: str): def get_multi_weights_row(self, prefix: str, quantize: str):
if quantize == "gptq": if quantize == "gptq":
try: try:
qweight = self.get_sharded(f"{prefix}.qweight", dim=0) qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError: except RuntimeError:
raise RuntimeError( raise RuntimeError("Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`")
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
)
qzeros = self.get_tensor(f"{prefix}.qzeros") qzeros = self.get_tensor(f"{prefix}.qzeros")
scales = self.get_tensor(f"{prefix}.scales") scales = self.get_tensor(f"{prefix}.scales")
g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0) g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)